3D cardiac motion estimation by point-constrained optical flow algorithm

In this paper, we propose a technique for 3D motion estimation of the left ventricle from an image sequence of a beating human heart. Accurate motion estimation of the cardiac wall has been shown to be very important in studying coronary diseases. The proposed technique requires initial 3D segmentation of the left ventricle area obtained for each time frame during the cardiac cycle. Characteristic points at its surface are detected, and matched in two consecutive frames by matching shape properties. Optical flow is computed from the sequence of images using the gradient-based Horn-Schunck method, additionally constrained with motion estimates for characteristic surface points. Our work demonstrates the application of the Horn-Schunck optical flow algorithm for 3D cardiac motion estimation, and proposes to improve the accuracy of estimation by introducing constraints obtained by a shape-based matching method.